Search Results for author: Ahmed M. Alaa

Found 30 papers, 13 papers with code

Conformal Time-series Forecasting

1 code implementation NeurIPS 2021 Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar

Current approaches for multi-horizon time series forecasting using recurrent neural networks (RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical application domains where an uncertainty estimate is also required.

Decision Making Time Series +1

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

no code implementations17 Feb 2021 Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar

In this paper, we introduce a 3-dimensional evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion.

Image Generation

Learning Matching Representations for Individualized Organ Transplantation Allocation

1 code implementation28 Jan 2021 Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar

Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients.

Representation Learning

Estimating Structural Target Functions using Machine Learning and Influence Functions

1 code implementation14 Aug 2020 Alicia Curth, Ahmed M. Alaa, Mihaela van der Schaar

Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.

Epidemiology

CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19

no code implementations27 Jul 2020 Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources.

Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions

1 code implementation ICML 2020 Ahmed M. Alaa, Mihaela van der Schaar

Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging.

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

no code implementations ICML 2020 Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, Mihaela van der Schaar

In this paper, we develop an approximate Bayesian inference scheme based on posterior regularisation, wherein unlabelled target data are used as "pseudo-labels" of model confidence that are used to regularise the model's loss on labelled source data.

Bayesian Inference Decision Making

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

1 code implementation NeurIPS 2020 Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context -- we treat each country as a distinct data point, and exploit variations of policies across countries to learn country-specific policy effects.

Gaussian Processes Variational Inference

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

3 code implementations ICLR 2020 Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.

Attentive State-Space Modeling of Disease Progression

1 code implementation NeurIPS 2019 Ahmed M. Alaa, Mihaela van der Schaar

Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics.

Predicting Patient Outcomes

Demystifying Black-box Models with Symbolic Metamodels

1 code implementation NeurIPS 2019 Ahmed M. Alaa, Mihaela van der Schaar

A symbolic metamodel is a model of a model, i. e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation.

The Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions

no code implementations25 Sep 2019 Ahmed M. Alaa, Mihaela van der Schaar

To address this question, we develop the discriminative jackknife (DJ), a formal inference procedure that constructs predictive confidence intervals for a wide range of deep learning models, is easy to implement, and provides rigorous theoretical guarantees on (1) and (2).

Lifelong Bayesian Optimization

no code implementations29 May 2019 Yao Zhang, James Jordon, Ahmed M. Alaa, Mihaela van der Schaar

In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time.

Model Selection

Forecasting Individualized Disease Trajectories using Interpretable Deep Learning

no code implementations24 Oct 2018 Ahmed M. Alaa, Mihaela van der Schaar

In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability.

Disease Trajectory Forecasting

AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning

no code implementations ICML 2018 Ahmed M. Alaa, Mihaela van der Schaar

AUTOPROGNOSIS optimizes ensembles of pipeline configurations efficiently using a novel batched Bayesian optimization (BO) algorithm that learns a low-dimensional decomposition of the pipelines high-dimensional hyperparameter space in concurrence with the BO procedure.

Meta-Learning

Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms

no code implementations24 Dec 2017 Ahmed M. Alaa, Mihaela van der Schaar

We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data, this is a central problem in various application domains, including healthcare, social sciences, and online advertising.

Causal Inference Selection bias

Deep Counterfactual Networks with Propensity-Dropout

1 code implementation19 Jun 2017 Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar

The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers.

Causal Inference Selection bias

Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model

no code implementations22 May 2017 Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar

We report the development and validation of a data-driven real-time risk score that provides timely assessments for the clinical acuity of ward patients based on their temporal lab tests and vital signs, which allows for timely intensive care unit (ICU) admissions.

Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis

no code implementations ICML 2017 Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar

Our model captures "informatively sampled" patient episodes: the clinicians' decisions on when to observe a hospitalized patient's vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient's latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process.

Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes

1 code implementation NeurIPS 2017 Ahmed M. Alaa, Mihaela van der Schaar

Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS).

Bayesian Inference Gaussian Processes +2

A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference

no code implementations18 Dec 2016 Ahmed M. Alaa, Mihaela van der Schaar

Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare.

A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data

no code implementations16 Nov 2016 Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar

Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU).

Personalized Donor-Recipient Matching for Organ Transplantation

no code implementations12 Nov 2016 Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar

Organ transplants can improve the life expectancy and quality of life for the recipient but carries the risk of serious post-operative complications, such as septic shock and organ rejection.

Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes

no code implementations27 Oct 2016 Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar

Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit (ICU) admissions for clinically deteriorating patients.

Gaussian Processes Transfer Learning

Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts

no code implementations3 May 2016 Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs.

Transfer Learning

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

no code implementations1 Feb 2016 Ahmed M. Alaa, Kyeong H. Moon, William Hsu, Mihaela van der Schaar

A cluster of patients is a set of patients with similar features (e. g. age, breast density, family history, etc.

Cannot find the paper you are looking for? You can Submit a new open access paper.